Prediction Markets for Science?

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There&#8217-s a set of Robin Hanson slides that are much more interesting than the presentation he gave at Yahoo! Confab.

– It&#8217-s bigger (67 slides vs. 21) and it covers the prediction market problematic in a more comprehensive way (including MSR).

– The decision market concept takes a minor place, whereas at Confab, Robin Hanson made the mistake to focus most of his speech on it &#8212-interesting concept but that was the wrong audience (Confab attendees wanted specific answers about basic prediction market questions).

– Here are excerpts, but I recommend you to download the presentation file, read it from A to Z, and share it with your friends and colleagues. (And if you had downloaded his Confab slides, direction the trash can of your computer &#8211-don&#8217-t keep 7 megas of useless bits of information.)


Prediction Markets for Science? – (PPT) – by Robin Hanson – 2006-12-XX


Today’s Science Prices (Play $ Alas) – [Foresight Exchange]
11-14% P != NP proven by 2010
16-18% Cancer cured by 2010
16-19% Cold fusion works by 2015
28-29% Mammal immortality by 2015
28-31% Eventual universe collapse
68-70% Fusion energy sold by 2045
74-76% Extraterrestrial life by 2050
91-95% A gamma ray burst w/in 33Mly
93-95% Cosmo constant &gt- 0
93-96% Neutrino mass &gt- 0

Science Decision Markets
E[ Iraq civil war | US moves troops out? ]
E[ Sea levels | Raise CO2 tax ]
E[ Lifespan | Health care reform ]

E[ Murders | More gun control ]
E[ Cancer deaths | More research funding ]
E[ Firm stock price | fire CEO? ]
E[ Succeed? | Fund project ]
E[ Publications | Hire candidate ]
E[ Citations | Publish ]

Theory I – Old
“Strong Efficient Markets” is straw man

No info – Supply and Demand
Assume beliefs not respond to prices
Price is weighted average of beliefs
More influence: risk takers, rich
Info, Static – Rational Expectations
Price clears, but beliefs depend on price
No trade if not expect “noise traders”
Price not reveal all info
More influence: info holders

Theory II – Market Microstructure
Info, Dynamic – Game Theory

Example – Kyle ’85
X – Informed trader(s) – risk averse
Y – Noise trader – fool or liquidity pref
Market makers – no info, deep pockets
If many compete, Price = E[value|x+y]
Info markets – use risk-neutral limit
If Y larger, X larger to compensate more info gathered, so more accuracy!

Theory III – Behavioral Finance
Humans are overconfident

Far more speculative trade than need
Mere fact of disagreement shows
Overconfidence varies with person, experience, consequence severity
Price in part an ave of beliefs?
Adds noise to price aggregates?
Prices more honest than talk, polls, …

Ask the Right Questions
Cost independent of topic, but value not!
Seek high value to more accurate estimates!
Relevant standard: beat existing institutions
Where suspect more accuracy is possible
Suspect info is withheld, or not sure who has it
Prefer fun, easy to explain and judge
Prefer can let many know best estimates
Not fear reveal secrets, use fear to motivate
Avoid inducing foul play

Eight Design Issues
How avoid self-defeating prophecies?
How handle billions of possible combos?
What if terrorists lose $ to mislead us?
What if terrorists gain $ by give us info?
How not alarm public, inform terrorists?
Price can mislead if deciders know more.
Will markets induce people to lie?
Will markets help employees embezzle?


Thoughts About &#8220-Decision Markets&#8221-:

Decision Market for Science

#1. Robin Hanson first wanted to apply his decision market concept to refine the democratic process &#8212-&#8221-vote on values but bet on beliefs&#8220-. He calls that &#8220-futarchy&#8221- (PDF) &#8212-as far as I can see, only some libertarian wackos like Chris Hibbert or Peter McCluskey bought the idea. (I&#8217-m not even sure his paper was accepted somewhere for publication.)

#2. Now, Robin Hanson tries to plug his decision market concept as a management decision tool &#8212-here&#8217-s from his Confab presentation:

Decision Market Applications

E[ Revenue | Switch ad agency? ]
E[ Revenue | Raise price 10%? ]
E[ Project done date | Drop feature? ]
E[ Project done date | Add personnel? ]
E[ Stock price | Fire CEO? ]
E[ Stock price | Acquire firm X? ]

The guy doesn&#8217-t have the slightest chance that his envisioned applications see the ray of light, one day (that is, before his head gets chopped off and frozen, shortly after his death). Basically, he wants the senior executives to be replaced with a market-generated automatism. Even if he can prove that it would lead to lead to better management decisions (and I trust him on that), he&#8217-ll encounter entrenched resistance from the same people his decision tool was created to compete with. I&#8217-d short-sell Robin Hanson on that one &#8212-with all my might, and I&#8217-d bet George Soros would see an opportunity here.

#3. The only chance the guy has would be to dig the field of management science for areas where a series of micro decisions are taken by mid-level executives or technologist or scientists or other employees &#8212-but NEVER by senior executives. In that perspective, all the examples of applications he gave above are worthless &#8212-direction the trash can of your computer (and select &#8220-empty the trash can&#8221-, to make sure they disappear for good.)

#4. Robin Hanson (who is a bright inventor) is totally incapable of seeing the light of what could be a successful innovation. The only chance the guy has would be for him to network with Silicon Valley&#8217-s geeks-turned-IT-executives, and, after a series of of pitches, maybe he would get feedback from someone who can find a mutant idea &#8212-an idea that is original, almost bizarre- an idea nobody ever thought of before. Robin Hanson, on his own, is totally incapable of thinking creatively in terms of innovation &#8212-he likes big ideas but he doesn&#8217-t get people and marketing (including internet usability). Which is why I&#8217-m suggesting to him to go West and to find his complement(s) there. That&#8217-s his only chance.

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